Centralized federated learning model based on model accuracy

doi:10.16511/j.cnki.qhdxxb.2022.26.002

Download: PDF(6739 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Existing federated learning models have problems due to malicious central servers and malicious participants publishing false data that poisons the model. A decentralized federated learning model was developed to address these problems by moving the aggregation work from the central server to the participants' computers. Each participant uses the aggregation algorithm to write the trained model parameters into the transaction and generates blocks that are then published to the blockchain network. A Byzantine fault-tolerant consensus algorithm based on model accuracy is used to build a consensus group and the nodes are dynamically joined by establishing a node information table. The results show that under the same conditions, compared with the traditional Byzantine fault-tolerant consensus algorithm, the throughput of the high-performance Byzantine fault-tolerant consensus algorithm based on model accuracy is increased by 60%, and the average system delay is reduced from 6 s to 1 s.

Keywords federal learning blockchain consensus mechanism model accuracy decentralized learning

Issue Date: 26 April 2022

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	SONG Yubo
	ZHU Jingkai
	ZHAO Lingqi
	HU Aiqun

Cite this article:

SONG Yubo,ZHU Jingkai,ZHAO Lingqi, et al. Centralized federated learning model based on model accuracy[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(5): 832-841.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2022.26.002 OR http://jst.tsinghuajournals.com/EN/Y2022/V62/I5/832

[1] MCMAHAN H B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]//Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). Fort Lauderdale, USA:JMLR, 2017:1273-1282.
[2] ZHANG J L, CHEN J J, WU D, et al. Poisoning attack in federated learning using generative adversarial nets[C]//Proceedings of the 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE). Rotorua, New Zealand:IEEE, 2019:374-380.
[3] LIU Y, YU J J Q, KANG J W, et al. Privacy-preserving traffic flow prediction:A federated learning approach[J]. IEEE Internet of Things Journal, 2020, 7(8):7751-7763.
[4] 赵灵奇, 宋宇波, 张克落, 等. 基于区块链和分层加密的物流隐私保护机制[J]. 应用科学学报, 2019, 37(2):224-234. ZHAO L Q, SONG Y B, ZHANG K L, et al. Logistics information privacy protection based on blockchain and hierarchical encryption[J]. Journal of Applied Sciences, 2019, 37(2):224-234. (in Chinese)
[5] FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia:JMLR, 2017:1126-1135.
[6] HUBERT E, TOMER K, BRENDAN M, et al. Semi-cyclic stochastic gradient descent[C]//Proceedings of the 36th International Conference on Machine Learning. Long Beach, USA:PMLR 97, 2019:1764-1773.
[7] WANG H Y, YUROCHKIN M, SUN Y K, et al. Federated learning with matched averaging[C]//Proceedings of the 8th International Conference on Learning Representations (ICLR). Addis Ababa, Ethiopia:ICLR, 2020:1-16.
[8] MENG W Z, TISCHHAUSER E W, WANG Q J, et al. When intrusion detection meets blockchain technology:A review[J]. IEEE Access, 2018(6):10179-10188.
[9] LU Y L, HUANG X H, DAI Y Y, et al. Blockchain and federated learning for privacy-preserved data sharing in industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2020, 16(6):4177-4186.
[10] NASR M, SHOKRI R, HOUMANSADR A. Comprehensive privacy analysis of deep learning:Passive and active white-box inference attacks against centralized and federated learning[C]//Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP). San Francisco, USA:IEEE, 2019:739-753.
[11] NAKAMOTO S. Bitcoin:A peer-to-peer electronic cash system[Z/OL].[2021-07-10]. https://bitcoin.org/en/.
[12] 宋宇波, 张仕奇, 宋睿. 一种基于投票权竞争的区块链共识机制[J]. 山东大学学报(理学版), 2020, 55(3):43-50. SONG Y B, ZHANG S Q, SONG R. A blockchain consensus mechanism based on voting rights competition[J]. Journal of Shandong University (Natural Science), 2020, 55(3):43-50. (in Chinese)
[13] AMOUSSOU-GUENOU Y, DEL POZZO A, POTOP-BUTUCARU M, et al. Dissecting tendermint[C]//Proceedings of the 7th International Conference on Networked Systems. Marrakech, Morocco:Springer, 2019:166-182.